Dynamics of waste proteins in brain tissue: Numerical insights into Alzheimer's risk factors

Over the past few decades, research has indicated that the buildup of waste proteins, like amyloid-β (Aβ), in the brain's interstitial spaces is linked to neurodegenerative diseases like Alzheimer's, but the details of how such proteins are removed from the brain are not well understood. We have developed a numerical model to investigate the aggregation and clearance mechanisms of Aβ in the interstitial spaces of the brain. The model describes the volume-averaged transport of Aβ in a segment of the brain interstitium modeled as a porous medium, oriented between the perivascular space (fluid-filled channel surrounding a blood vessel) of a penetrating arteriole and that of a venule. Our numerical approach solves N coupled advection-diffusion-aggregation equations that model the production, aggregation, fragmentation, and clearance of N species of Aβ. We simulate N=50 species to investigate the oligomer-size dependence of clearance and aggregation. We introduce a timescale plot that helps predict Aβ buildup for different neurological conditions. We show that a sudden increase in monomer concentration, as occurs in conditions like traumatic brain injury, leads to significant plaque formation, which can qualitatively be predicted using the timescale plot. Our results also indicate that impaired protein clearance (as occurs with aging) and fragmentation are both mechanisms that sustain large intermediate oligomer concentrations. Our results provide novel insight into several known risk factors for Alzheimer's disease and cognitive decline, and we introduce a unique framing of Aβ dynamics as a competition between different timescales associated with production rates, aggregation rates, and clearance conditions.

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Publication Details

Journal
Physical review. E
Published
2024-09-03
DOI
https://doi.org/10.1103/physreve.110.034401
Citations
1
Primary Topic
Alzheimer's disease research and treatments
Type
article
Field-Weighted Citation Impact
0.33

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article

Dynamics of waste proteins in brain tissue: Numerical insights into Alzheimer's risk factors

Jeffrey Tithof, Saikat Mukherjee, L. Watkins
1 citations
Physical review. E
Alzheimer's disease research and treatments
0.33
article

Dynamics of waste proteins in brain tissue: Numerical insights into Alzheimer's risk factors

Jeffrey Tithof, Saikat Mukherjee, L. Watkins
article en
1 citations

Abstract

Over the past few decades, research has indicated that the buildup of waste proteins, like amyloid-β (Aβ), in the brain's interstitial spaces is linked to neurodegenerative diseases like Alzheimer's, but the details of how such proteins are removed from the brain are not well understood. We have developed a numerical model to investigate the aggregation and clearance mechanisms of Aβ in the interstitial spaces of the brain. The model describes the volume-averaged transport of Aβ in a segment of the brain interstitium modeled as a porous medium, oriented between the perivascular space (fluid-filled channel surrounding a blood vessel) of a penetrating arteriole and that of a venule. Our numerical approach solves N coupled advection-diffusion-aggregation equations that model the production, aggregation, fragmentation, and clearance of N species of Aβ. We simulate N=50 species to investigate the oligomer-size dependence of clearance and aggregation. We introduce a timescale plot that helps predict Aβ buildup for different neurological conditions. We show that a sudden increase in monomer concentration, as occurs in conditions like traumatic brain injury, leads to significant plaque formation, which can qualitatively be predicted using the timescale plot. Our results also indicate that impaired protein clearance (as occurs with aging) and fragmentation are both mechanisms that sustain large intermediate oligomer concentrations. Our results provide novel insight into several known risk factors for Alzheimer's disease and cognitive decline, and we introduce a unique framing of Aβ dynamics as a competition between different timescales associated with production rates, aggregation rates, and clearance conditions.

Physical review. EVol. 110(3)
University of Minnesota (US), Iowa State University (US), University of Minnesota System (US)
Burroughs Wellcome Fund, University of Minnesota
Openalex Percentile: Top 38%
Alzheimer's disease research and treatments
0.33
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